Joint Beamforming for Multi-user Multi-target FD ISAC System: A Hybrid GRQ-GA Approach

TL;DR

This paper introduces a hybrid GRQ-GA approach for joint beamforming in a full-duplex ISAC system, optimizing communication and sensing performance with significant sum-rate improvements over baseline methods.

Contribution

It proposes a novel hybrid optimization method combining closed-form GRQ solutions and genetic algorithms for beamforming in multi-user multi-target FD ISAC systems.

Findings

Achieves up to 98% higher sum-rate than half-duplex systems.

Demonstrates superior performance over existing benchmark algorithms.

Provides insights into sensing and communication trade-offs.

Abstract

In this paper, we consider a full-duplex (FD) Integrated Sensing and Communication (ISAC) system, in which the base station (BS) performs downlink and uplink communications with multiple users while simultaneously sensing multiple targets. In the scope of this work, we assume a narrowband and static scenario, aiming to focus on the beamforming and power allocation strategies. We propose a joint beamforming strategy for designing transmit and receive beamformer vectors at the BS. The optimization problem aims to maximize the communication sum-rate, which is critical for ensuring high-quality service to users, while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage. The optimal receive beamformers are first derived using a closed-form Generalized Rayleigh Quotient (GRQ) solution, reducing the variables to be optimized. Then, the remaining problem is solved using floating-point Genetic Algorithms (GA). The numerical results show that the proposed GA-based solution demonstrates up to a 98% enhancement in sum-rate compared to a baseline half-duplex ISAC system and provides better performance than a benchmark algorithm from the literature. Additionally, it offers insights into sensing performance effects on beam patterns as well as communicationsensing trade-offs in multi-target scenarios.